Systems have attempted to use various neural networks and computer learning algorithms to identify objects within an image or a series of images. However, existing attempts to identify objects are not successful because the methods of pattern recognition and estimating location of objects are inaccurate and non-general. Furthermore, existing systems attempt to identify objects by some sort of pattern recognition that is too specific, or not sufficiently adaptable, as the object changes between images. Thus, there is a need for an enhanced method for training a neural network to detect and identify objects of interest with increased accuracy by utilizing improved computational operations.